




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Dict				)       ->   str:






      '''simple docstring'''


      if a < 0 or b < 0:
            raise ValueError('the value of both inputs must be positive'				)
      _A     =  str(bin(_snake_case				)				)[2:]  # remove the leading "0b"
      _A     =  str(bin(_snake_case				)				)[2:]
      _A     =  max(len(_snake_case				)							,    len(_snake_case				)				)
      return "0b" + "".join(
          str(int('1' in (char_a, char_b)				)				)
          for char_a, char_b in zip(a_binary.zfill(_snake_case				)							,    b_binary.zfill(_snake_case				)				)				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






